Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Measuring the influence of moods on stock market using Twitter analysis(Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy†, “Sadness†, “Fear†, “Anger†, “Trust†, “Disgust†, “Surprise†and “Anticipation†. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. © Springer Nature Singapore Pte Ltd. 2019.Item Accurate Router Level Estimation of Network-on-Chip Architectures using Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2019) Kumar, A.; Talawar, B.The problem of intra-communication between the Intellectual Properties(IPs) due to the rise in the amount of cores on single chips in System-on-Chip(SoC). Network-on-Chips(NoCs) has emerged as a reliable on-chip communication framework for Chip Multiprocessors and SoCs. Estimating NoC power and performance in the early stages has become crucial. We employ Machine Learning(ML) approaches to estimate architecture-level on-chip router models and performance. Experiments were carried out with distinct topology sizes with various virtual channels, injection rates, and traffic patterns. Booksim and Orion simulators are used to validate the results. Approximately 6% to 8% prediction error and a minimum speedup of 1500 × to 2000 × were shown in the framework. © 2019 IEEE.Item A Support Vector Regression-Based Approach to Predict the Performance of 2D 3D On-Chip Communication Architectures(Institute of Electrical and Electronics Engineers Inc., 2019) Nirmal Kumar, A.; Talawar, B.Recently, Networks-on-Chips (NoCs) have evolved as a scalable solution to traditional bus and point-to-point architecture. NoC design performance evaluation is largely based on simulation, which is extremely slow as the architecture size increases, and it gives little insight on how distinct design parameters impact the actual performance of the network. Simulation for optimization purposes is therefore very difficult to use. In this paper, we propose a Support Vector Regression(SVR)-based framework, which can be used to analyze the performance of 2D and 3D NoC architectures. Experiments were conducted by varying architecture sizes with different virtual channels, injection rates. The framework proposed can be used to obtain fast and accurate NoC performance estimates with a prediction error 2% to 4% and minimum speedup of 3000 × to 3500×. © 2019 IEEE.Item Assessment and Prediction of Specific Energy Using Rock Brittleness in Rock Cutting(Springer Nature, 2020) Raghavan, V.; Murthy, C.S.N.In this study, we used picks with point attack angles of 45°, 50°, 55°, and 65° and 45°, 55°, and 65° attack angles in rock cutting experiments. The main objective is to estimate specific energy during the cutting process based on rock brittleness and study the influence of attack angle on specific energy. From the experimental data, we compared the obtained results using multiple linear regressions and ANOVA to predict the specific energy and found that the model developed were statistically significant. R2 of the brittleness B4 is 0.79 in comparision with R2 of density, UCS, BTS and abrasivity as 0.74, 0.83, 0.84 and 0.73. Specific energy not only be predicted from density, UCS, BTS, abrasivity, it can also be predicted using rock brittleness. © 2020, Springer Nature Switzerland AG.Item Bayesian optimization and gradient boosting to detect phishing websites(Institute of Electrical and Electronics Engineers Inc., 2021) Pavan, R.; Nara, M.; Gopinath, S.; Patil, N.We propose an Extreme Gradient Boosting framework for classification and regression problems emerging in machine learning for small-sized data sources sampled from a discrete distribution, i.e. data containing discrete or quantized attributes. The model parameters are iteratively refined from a prior belief for specific use cases using Bayesian optimization. We focus the application area of this framework on detecting fraudulent websites. With properly stated reasoning, we empirically test our methodology on a publicly available and bench-marked UCI Phishing dataset to demonstrate the superior performance of this approach as compared to existing methods in the literature. © 2021 IEEE.Item Smart Energy Meter Calibration: An Edge Computation Method: Poster(Association for Computing Machinery, Inc, 2021) Dubara, H.V.; Parihar, M.; Ramamritham, K.Smart meters are the backbone of smart grids. They provide real time electricity consumption data and and are widely used for measuring, monitoring and analyzing energy consumption. Sometimes, they enable users to perform corrective actions. But, to facilitate proper data analysis, it is imperative that data be accurate or have minimum error. This paper presents an edge deployed smart meter error correction algorithm that utilises Clustering (using K-Means algorithm) and Feed-Forward Artificial Neural Networks (ANN). An edge device, a Raspberry Pi Module, connects smart meters to the internet. The algorithm maps (possibly erroneous) readings of our in-house developed meters to readings of calibrated standard off-the-shelf (Schneider) meters. Usage of Clustering with ANN has helped substantially improve the accuracy of the readings from a previously used linear regression designed for the same purpose. An accuracy of 70-75% was achieved while using linear regression, whereas the proposed algorithm obtains accuracy in the range of 84.47-88%. The neural networks are also less complex, making them suitable for deployment in Raspberry Pi 3B based embedded hardware systems. © 2021 ACM.Item Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey(Springer Science and Business Media Deutschland GmbH, 2023) Priyadarshini, R.; Sudhakara, B.; Kamath S․, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Using Stacking Ensemble Method for Rental Bike Prediction(Springer Science and Business Media Deutschland GmbH, 2025) Akashdeep, S.; Mahalinga, A.N.; Harshvardhan, R.; Chinnahalli KomariGowda, S.; Patil, N.Rental bike platforms that improve mobility comfort are on the rise in major cities worldwide. One of the essential requirements for these rental bike systems is that bikes are available to end users at the specified time, reducing waiting time. Increased waiting time indicates that movement has been halted, implying that more efficiency can be gained. As a result, the city’s main priority is ensuring a steady supply of bicycles. It’s crucial to be able to forecast the number of bikes needed at each hour for this. This work look at alternative models for forecasting the bike count per hour needed to maintain a steady supply of bikes. Weather data (Temperature, Humidity, Wind speed, Dew point), the quantity of bikes hired every hour, and time information are all used to train the models. Filtering can also be used to exclude non-predictive parameters and rank features based on how well they predict outcomes. The effectiveness of the regression model was assessed using a testing set after they had been trained using repeated cross-validation. For the model Gradient Boosting Machine, the optimum R2 value is 0.96. The most significant predictors are also determined, as well as their relationships. Bike-sharing demand, data mining, predictive analytics, public bikes, regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.